Overview

Dataset statistics

Number of variables23
Number of observations52
Missing cells603
Missing cells (%)50.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory186.5 B

Variable types

Numeric14
Unsupported9

Alerts

Year is highly overall correlated with Natural gas and 11 other fieldsHigh correlation
Natural gas is highly overall correlated with Year and 11 other fieldsHigh correlation
Coal is highly overall correlated with Year and 11 other fieldsHigh correlation
Hydroenergy is highly overall correlated with Year and 10 other fieldsHigh correlation
Nuclear is highly overall correlated with Year and 10 other fieldsHigh correlation
Firewood is highly overall correlated with Year and 9 other fieldsHigh correlation
Other Primary_x000d_ is highly overall correlated with Year and 10 other fieldsHigh correlation
Total Primaries is highly overall correlated with Year and 11 other fieldsHigh correlation
Electricity is highly overall correlated with Year and 11 other fieldsHigh correlation
Diesel oil is highly overall correlated with Year and 8 other fieldsHigh correlation
Other secondary is highly overall correlated with Year and 9 other fieldsHigh correlation
Total Secundaries is highly overall correlated with Year and 11 other fieldsHigh correlation
Total is highly overall correlated with Year and 11 other fieldsHigh correlation
Oil has 52 (100.0%) missing valuesMissing
Natural gas has 18 (34.6%) missing valuesMissing
Nuclear has 14 (26.9%) missing valuesMissing
Firewood has 33 (63.5%) missing valuesMissing
Sugarcane and products has 52 (100.0%) missing valuesMissing
Other Primary_x000d_ has 30 (57.7%) missing valuesMissing
LPG has 52 (100.0%) missing valuesMissing
Gasoline/alcohol has 52 (100.0%) missing valuesMissing
Kerosene/jet fuel has 52 (100.0%) missing valuesMissing
Coke has 52 (100.0%) missing valuesMissing
Charcoal has 52 (100.0%) missing valuesMissing
Gases has 52 (100.0%) missing valuesMissing
Other secondary has 40 (76.9%) missing valuesMissing
Non-energy has 52 (100.0%) missing valuesMissing
Year is uniformly distributedUniform
Year has unique valuesUnique
Coal has unique valuesUnique
Hydroenergy has unique valuesUnique
Total Primaries has unique valuesUnique
Electricity has unique valuesUnique
Fuel oil has unique valuesUnique
Total Secundaries has unique valuesUnique
Total has unique valuesUnique
Oil is an unsupported type, check if it needs cleaning or further analysisUnsupported
Sugarcane and products is an unsupported type, check if it needs cleaning or further analysisUnsupported
LPG is an unsupported type, check if it needs cleaning or further analysisUnsupported
Gasoline/alcohol is an unsupported type, check if it needs cleaning or further analysisUnsupported
Kerosene/jet fuel is an unsupported type, check if it needs cleaning or further analysisUnsupported
Coke is an unsupported type, check if it needs cleaning or further analysisUnsupported
Charcoal is an unsupported type, check if it needs cleaning or further analysisUnsupported
Gases is an unsupported type, check if it needs cleaning or further analysisUnsupported
Non-energy is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-07-30 07:35:54.129075
Analysis finished2023-07-30 07:36:41.065819
Duration46.94 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Year
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1995.5
Minimum1970
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:36:41.228041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile1972.55
Q11982.75
median1995.5
Q32008.25
95-th percentile2018.45
Maximum2021
Range51
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation15.154757
Coefficient of variation (CV)0.0075944662
Kurtosis-1.2
Mean1995.5
Median Absolute Deviation (MAD)13
Skewness0
Sum103766
Variance229.66667
MonotonicityStrictly increasing
2023-07-30T07:36:41.495978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1970 1
 
1.9%
1971 1
 
1.9%
1998 1
 
1.9%
1999 1
 
1.9%
2000 1
 
1.9%
2001 1
 
1.9%
2002 1
 
1.9%
2003 1
 
1.9%
2004 1
 
1.9%
2005 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
1970 1
1.9%
1971 1
1.9%
1972 1
1.9%
1973 1
1.9%
1974 1
1.9%
1975 1
1.9%
1976 1
1.9%
1977 1
1.9%
1978 1
1.9%
1979 1
1.9%
ValueCountFrequency (%)
2021 1
1.9%
2020 1
1.9%
2019 1
1.9%
2018 1
1.9%
2017 1
1.9%
2016 1
1.9%
2015 1
1.9%
2014 1
1.9%
2013 1
1.9%
2012 1
1.9%

Oil
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Natural gas
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)94.1%
Missing18
Missing (%)34.6%
Infinite0
Infinite (%)0.0%
Mean-3927.8979
Minimum-14183.44
Maximum-0.85
Zeros0
Zeros (%)0.0%
Negative34
Negative (%)65.4%
Memory size548.0 B
2023-07-30T07:36:41.728752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-14183.44
5-th percentile-13376.969
Q1-7516.0375
median-2007.99
Q3-31.3775
95-th percentile-1.669
Maximum-0.85
Range14182.59
Interquartile range (IQR)7484.66

Descriptive statistics

Standard deviation4618.947
Coefficient of variation (CV)-1.1759336
Kurtosis-0.28792389
Mean-3927.8979
Median Absolute Deviation (MAD)2002.475
Skewness-1.015186
Sum-133548.53
Variance21334671
MonotonicityNot monotonic
2023-07-30T07:36:41.960296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
-0.85 2
 
3.8%
-7.64 2
 
3.8%
-14183.44 1
 
1.9%
-1569.67 1
 
1.9%
-4806.51 1
 
1.9%
-2889.8 1
 
1.9%
-6682.24 1
 
1.9%
-11543.61 1
 
1.9%
-8717.87 1
 
1.9%
-13670.34 1
 
1.9%
Other values (22) 22
42.3%
(Missing) 18
34.6%
ValueCountFrequency (%)
-14183.44 1
1.9%
-13670.34 1
1.9%
-13219 1
1.9%
-11543.61 1
1.9%
-10325.21 1
1.9%
-9362.71 1
1.9%
-8717.87 1
1.9%
-8069.18 1
1.9%
-7793.97 1
1.9%
-6682.24 1
1.9%
ValueCountFrequency (%)
-0.85 2
3.8%
-2.11 1
1.9%
-4.79 1
1.9%
-5.09 1
1.9%
-5.94 1
1.9%
-7.64 2
3.8%
-27.21 1
1.9%
-43.88 1
1.9%
-73.69 1
1.9%
-107.83 1
1.9%

Coal
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1721.7685
Minimum-4253.56
Maximum-401.51
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)100.0%
Memory size548.0 B
2023-07-30T07:36:42.220342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4253.56
5-th percentile-4018.411
Q1-2077.7775
median-1525.495
Q3-834.5475
95-th percentile-436.4195
Maximum-401.51
Range3852.05
Interquartile range (IQR)1243.23

Descriptive statistics

Standard deviation1132.28
Coefficient of variation (CV)-0.65762615
Kurtosis-0.30795057
Mean-1721.7685
Median Absolute Deviation (MAD)657.535
Skewness-0.86288362
Sum-89531.96
Variance1282057.9
MonotonicityNot monotonic
2023-07-30T07:36:42.484023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-454.51 1
 
1.9%
-473.18 1
 
1.9%
-1444.42 1
 
1.9%
-3242.64 1
 
1.9%
-3294.6 1
 
1.9%
-3264.65 1
 
1.9%
-1463.27 1
 
1.9%
-1535.82 1
 
1.9%
-1717.8 1
 
1.9%
-1832.46 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
-4253.56 1
1.9%
-4189.67 1
1.9%
-4129.61 1
1.9%
-3927.43 1
1.9%
-3559.56 1
1.9%
-3544.06 1
1.9%
-3329.31 1
1.9%
-3294.6 1
1.9%
-3264.65 1
1.9%
-3242.64 1
1.9%
ValueCountFrequency (%)
-401.51 1
1.9%
-412.71 1
1.9%
-426.03 1
1.9%
-444.92 1
1.9%
-454.51 1
1.9%
-473.18 1
1.9%
-504.88 1
1.9%
-518.12 1
1.9%
-638.73 1
1.9%
-657.23 1
1.9%

Hydroenergy
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-20518.364
Minimum-34796.89
Maximum-3302.28
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)100.0%
Memory size548.0 B
2023-07-30T07:36:42.840894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-34796.89
5-th percentile-32634.977
Q1-29847.658
median-22002.94
Q3-12549.71
95-th percentile-4566.1605
Maximum-3302.28
Range31494.61
Interquartile range (IQR)17297.948

Descriptive statistics

Standard deviation9638.1209
Coefficient of variation (CV)-0.46973145
Kurtosis-1.1949485
Mean-20518.364
Median Absolute Deviation (MAD)8175.955
Skewness0.2911697
Sum-1066954.9
Variance92893374
MonotonicityNot monotonic
2023-07-30T07:36:43.238575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3302.28 1
 
1.9%
-3599.12 1
 
1.9%
-24559.29 1
 
1.9%
-24699.38 1
 
1.9%
-25666.15 1
 
1.9%
-22580.15 1
 
1.9%
-23528.41 1
 
1.9%
-25238.2 1
 
1.9%
-26472.41 1
 
1.9%
-27885.2 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
-34796.89 1
1.9%
-33976.84 1
1.9%
-32821.95 1
1.9%
-32482 1
1.9%
-32465.96 1
1.9%
-31884.36 1
1.9%
-31744.82 1
1.9%
-31710.29 1
1.9%
-30840.26 1
1.9%
-30819.43 1
1.9%
ValueCountFrequency (%)
-3302.28 1
1.9%
-3599.12 1
1.9%
-4232.69 1
1.9%
-4839 1
1.9%
-5500.16 1
1.9%
-6050.86 1
1.9%
-6935.88 1
1.9%
-7812.81 1
1.9%
-8596.22 1
1.9%
-9795.95 1
1.9%

Nuclear
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)100.0%
Missing14
Missing (%)26.9%
Infinite0
Infinite (%)0.0%
Mean-2129.1611
Minimum-4203.27
Maximum-2.58
Zeros0
Zeros (%)0.0%
Negative38
Negative (%)73.1%
Memory size548.0 B
2023-07-30T07:36:43.670685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4203.27
5-th percentile-4141.144
Q1-3821.7375
median-3121.63
Q3-92.4125
95-th percentile-14.395
Maximum-2.58
Range4200.69
Interquartile range (IQR)3729.325

Descriptive statistics

Standard deviation1834.2937
Coefficient of variation (CV)-0.86151009
Kurtosis-1.944345
Mean-2129.1611
Median Absolute Deviation (MAD)1035.39
Skewness0.18628677
Sum-80908.12
Variance3364633.5
MonotonicityNot monotonic
2023-07-30T07:36:44.103545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
-4179.7 1
 
1.9%
-2482.01 1
 
1.9%
-3581.88 1
 
1.9%
-3213.03 1
 
1.9%
-3640.71 1
 
1.9%
-3376.67 1
 
1.9%
-3779.7 1
 
1.9%
-4080.93 1
 
1.9%
-4026.28 1
 
1.9%
-3436.73 1
 
1.9%
Other values (28) 28
53.8%
(Missing) 14
26.9%
ValueCountFrequency (%)
-4203.27 1
1.9%
-4179.7 1
1.9%
-4134.34 1
1.9%
-4101.73 1
1.9%
-4084.71 1
1.9%
-4080.93 1
1.9%
-4026.28 1
1.9%
-4007.72 1
1.9%
-3839.81 1
1.9%
-3835.75 1
1.9%
ValueCountFrequency (%)
-2.58 1
1.9%
-4.28 1
1.9%
-16.18 1
1.9%
-18.75 1
1.9%
-30.77 1
1.9%
-39.98 1
1.9%
-46.77 1
1.9%
-54.71 1
1.9%
-68.81 1
1.9%
-90.15 1
1.9%

Firewood
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)100.0%
Missing33
Missing (%)63.5%
Infinite0
Infinite (%)0.0%
Mean-43.737895
Minimum-71.39
Maximum-1.55
Zeros0
Zeros (%)0.0%
Negative19
Negative (%)36.5%
Memory size548.0 B
2023-07-30T07:36:44.498725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-71.39
5-th percentile-69.059
Q1-64.87
median-45.55
Q3-26.185
95-th percentile-6.851
Maximum-1.55
Range69.84
Interquartile range (IQR)38.685

Descriptive statistics

Standard deviation22.725883
Coefficient of variation (CV)-0.51959251
Kurtosis-1.1191833
Mean-43.737895
Median Absolute Deviation (MAD)20.03
Skewness0.46869152
Sum-831.02
Variance516.46574
MonotonicityNot monotonic
2023-07-30T07:36:44.884764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
-1.55 1
 
1.9%
-65.58 1
 
1.9%
-71.39 1
 
1.9%
-68.8 1
 
1.9%
-63.25 1
 
1.9%
-43.14 1
 
1.9%
-65.73 1
 
1.9%
-67.72 1
 
1.9%
-64.16 1
 
1.9%
-44.97 1
 
1.9%
Other values (9) 9
 
17.3%
(Missing) 33
63.5%
ValueCountFrequency (%)
-71.39 1
1.9%
-68.8 1
1.9%
-67.72 1
1.9%
-65.73 1
1.9%
-65.58 1
1.9%
-64.16 1
1.9%
-63.25 1
1.9%
-56.11 1
1.9%
-48.86 1
1.9%
-45.55 1
1.9%
ValueCountFrequency (%)
-1.55 1
1.9%
-7.44 1
1.9%
-13.92 1
1.9%
-18.87 1
1.9%
-23.24 1
1.9%
-29.13 1
1.9%
-31.61 1
1.9%
-43.14 1
1.9%
-44.97 1
1.9%
-45.55 1
1.9%

Sugarcane and products
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Other Primary_x000d_
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)95.5%
Missing30
Missing (%)57.7%
Infinite0
Infinite (%)0.0%
Mean-1595.23
Minimum-7138.42
Maximum-1
Zeros0
Zeros (%)0.0%
Negative22
Negative (%)42.3%
Memory size548.0 B
2023-07-30T07:36:45.259483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-7138.42
5-th percentile-5668.1225
Q1-2772.4675
median-241.815
Q3-22.3075
95-th percentile-10.248
Maximum-1
Range7137.42
Interquartile range (IQR)2750.16

Descriptive statistics

Standard deviation2281.8065
Coefficient of variation (CV)-1.4303934
Kurtosis0.34828257
Mean-1595.23
Median Absolute Deviation (MAD)236.315
Skewness-1.3052396
Sum-35095.06
Variance5206640.9
MonotonicityDecreasing
2023-07-30T07:36:45.615165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
-14.99 2
 
3.8%
-520.89 1
 
1.9%
-5676.99 1
 
1.9%
-5499.64 1
 
1.9%
-4620.22 1
 
1.9%
-3895.16 1
 
1.9%
-3028.94 1
 
1.9%
-2003.05 1
 
1.9%
-1166.92 1
 
1.9%
-625.21 1
 
1.9%
Other values (11) 11
 
21.2%
(Missing) 30
57.7%
ValueCountFrequency (%)
-7138.42 1
1.9%
-5676.99 1
1.9%
-5499.64 1
1.9%
-4620.22 1
1.9%
-3895.16 1
1.9%
-3028.94 1
1.9%
-2003.05 1
1.9%
-1166.92 1
1.9%
-625.21 1
1.9%
-520.89 1
1.9%
ValueCountFrequency (%)
-1 1
1.9%
-10 1
1.9%
-14.96 1
1.9%
-14.99 2
3.8%
-18.22 1
1.9%
-34.57 1
1.9%
-62.92 1
1.9%
-113.01 1
1.9%
-151.33 1
1.9%
-193.51 1
1.9%

Total Primaries
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-27055.185
Minimum-58191.16
Maximum-3756.79
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)100.0%
Memory size548.0 B
2023-07-30T07:36:45.916467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-58191.16
5-th percentile-53243.743
Q1-38955.067
median-23933.925
Q3-13257.922
95-th percentile-5038.058
Maximum-3756.79
Range54434.37
Interquartile range (IQR)25697.145

Descriptive statistics

Standard deviation16456.813
Coefficient of variation (CV)-0.60826837
Kurtosis-1.1170165
Mean-27055.185
Median Absolute Deviation (MAD)12802.6
Skewness-0.35626266
Sum-1406869.6
Variance2.7082669 × 108
MonotonicityNot monotonic
2023-07-30T07:36:46.200411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3756.79 1
 
1.9%
-4072.3 1
 
1.9%
-26214.28 1
 
1.9%
-28210.58 1
 
1.9%
-31035.11 1
 
1.9%
-30864.56 1
 
1.9%
-30528.71 1
 
1.9%
-31978.29 1
 
1.9%
-34253.78 1
 
1.9%
-35119 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
-58191.16 1
1.9%
-54932.27 1
1.9%
-53689.38 1
1.9%
-52879.13 1
1.9%
-52286.06 1
1.9%
-52153.23 1
1.9%
-51599.49 1
1.9%
-51563.65 1
1.9%
-50691.99 1
1.9%
-47580.88 1
1.9%
ValueCountFrequency (%)
-3756.79 1
1.9%
-4072.3 1
1.9%
-4737.56 1
1.9%
-5283.92 1
1.9%
-5926.2 1
1.9%
-6463.57 1
1.9%
-7337.38 1
1.9%
-8330.93 1
1.9%
-9480.13 1
1.9%
-10530.65 1
1.9%

Electricity
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23961.181
Minimum3613.39
Maximum46508.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:36:46.467214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3613.39
5-th percentile4906.9575
Q112936.685
median22951.87
Q335161.013
95-th percentile43475.643
Maximum46508.5
Range42895.11
Interquartile range (IQR)22224.328

Descriptive statistics

Standard deviation13081.425
Coefficient of variation (CV)0.5459424
Kurtosis-1.2132818
Mean23961.181
Median Absolute Deviation (MAD)11309.015
Skewness0.13521297
Sum1245981.4
Variance1.7112367 × 108
MonotonicityNot monotonic
2023-07-30T07:36:46.733436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3613.39 1
 
1.9%
4112.59 1
 
1.9%
25829.2 1
 
1.9%
26707.88 1
 
1.9%
27844.17 1
 
1.9%
25902.98 1
 
1.9%
26724.24 1
 
1.9%
28240.64 1
 
1.9%
29985.81 1
 
1.9%
31156.29 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
3613.39 1
1.9%
4112.59 1
1.9%
4564.17 1
1.9%
5187.42 1
1.9%
5768.89 1
1.9%
6350.11 1
1.9%
7266.76 1
1.9%
8173.78 1
1.9%
9106.34 1
1.9%
10223.54 1
1.9%
ValueCountFrequency (%)
46508.5 1
1.9%
44947.39 1
1.9%
44163.05 1
1.9%
42913.22 1
1.9%
42721.55 1
1.9%
42133.96 1
1.9%
41721.75 1
1.9%
41633.34 1
1.9%
41208.58 1
1.9%
40825.1 1
1.9%

LPG
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Gasoline/alcohol
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Kerosene/jet fuel
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Diesel oil
Real number (ℝ)

Distinct51
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-898.22115
Minimum-2907.78
Maximum-111.45
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)100.0%
Memory size548.0 B
2023-07-30T07:36:46.984611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2907.78
5-th percentile-2131.1435
Q1-1353.0175
median-825.85
Q3-297.8725
95-th percentile-118.783
Maximum-111.45
Range2796.33
Interquartile range (IQR)1055.145

Descriptive statistics

Standard deviation675.92556
Coefficient of variation (CV)-0.75251575
Kurtosis0.26229556
Mean-898.22115
Median Absolute Deviation (MAD)528.575
Skewness-0.8315191
Sum-46707.5
Variance456875.37
MonotonicityNot monotonic
2023-07-30T07:36:47.237427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-119.17 2
 
3.8%
-115.74 1
 
1.9%
-1357.24 1
 
1.9%
-1660.58 1
 
1.9%
-1186.85 1
 
1.9%
-1210.44 1
 
1.9%
-1074.01 1
 
1.9%
-1351.61 1
 
1.9%
-1671.63 1
 
1.9%
-1761.5 1
 
1.9%
Other values (41) 41
78.8%
ValueCountFrequency (%)
-2907.78 1
1.9%
-2345.34 1
1.9%
-2257.77 1
1.9%
-2027.54 1
1.9%
-1778.08 1
1.9%
-1761.5 1
1.9%
-1671.63 1
1.9%
-1660.58 1
1.9%
-1656.82 1
1.9%
-1593.12 1
1.9%
ValueCountFrequency (%)
-111.45 1
1.9%
-115.74 1
1.9%
-118.31 1
1.9%
-119.17 2
3.8%
-124.31 1
1.9%
-137.17 1
1.9%
-148.32 1
1.9%
-167.18 1
1.9%
-246.57 1
1.9%
-264.91 1
1.9%

Fuel oil
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-848.87038
Minimum-3313.85
Maximum-265.93
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)100.0%
Memory size548.0 B
2023-07-30T07:36:47.494362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3313.85
5-th percentile-1810.5565
Q1-1021.4175
median-630.795
Q3-423.525
95-th percentile-274.6555
Maximum-265.93
Range3047.92
Interquartile range (IQR)597.8925

Descriptive statistics

Standard deviation631.09023
Coefficient of variation (CV)-0.74344711
Kurtosis5.464735
Mean-848.87038
Median Absolute Deviation (MAD)268.49
Skewness-2.1270271
Sum-44141.26
Variance398274.88
MonotonicityNot monotonic
2023-07-30T07:36:47.738140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-652.61 1
 
1.9%
-1118.36 1
 
1.9%
-785.01 1
 
1.9%
-1421.45 1
 
1.9%
-1615.37 1
 
1.9%
-1640.98 1
 
1.9%
-1019.45 1
 
1.9%
-544.68 1
 
1.9%
-284.5 1
 
1.9%
-415.37 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
-3313.85 1
1.9%
-2981.73 1
1.9%
-1967.62 1
1.9%
-1682.05 1
1.9%
-1656.49 1
1.9%
-1640.98 1
1.9%
-1615.37 1
1.9%
-1421.45 1
1.9%
-1375.41 1
1.9%
-1169.42 1
1.9%
ValueCountFrequency (%)
-265.93 1
1.9%
-270.73 1
1.9%
-272.45 1
1.9%
-276.46 1
1.9%
-284.5 1
1.9%
-284.73 1
1.9%
-333.78 1
1.9%
-337.01 1
1.9%
-340.27 1
1.9%
-342.67 1
1.9%

Coke
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Charcoal
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Gases
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Other secondary
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing40
Missing (%)76.9%
Infinite0
Infinite (%)0.0%
Mean-70.098333
Minimum-137.32
Maximum-0.01
Zeros0
Zeros (%)0.0%
Negative12
Negative (%)23.1%
Memory size548.0 B
2023-07-30T07:36:47.961408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-137.32
5-th percentile-107.4495
Q1-79.615
median-75.14
Q3-67.1925
95-th percentile-20.3215
Maximum-0.01
Range137.31
Interquartile range (IQR)12.4225

Descriptive statistics

Standard deviation32.067451
Coefficient of variation (CV)-0.45746382
Kurtosis2.8076888
Mean-70.098333
Median Absolute Deviation (MAD)5.51
Skewness0.25451691
Sum-841.18
Variance1028.3214
MonotonicityNot monotonic
2023-07-30T07:36:48.154653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
-0.01 1
 
1.9%
-36.94 1
 
1.9%
-52.86 1
 
1.9%
-75.29 1
 
1.9%
-78.58 1
 
1.9%
-74.99 1
 
1.9%
-72.19 1
 
1.9%
-83.01 1
 
1.9%
-82.72 1
 
1.9%
-137.32 1
 
1.9%
Other values (2) 2
 
3.8%
(Missing) 40
76.9%
ValueCountFrequency (%)
-137.32 1
1.9%
-83.01 1
1.9%
-82.72 1
1.9%
-78.58 1
1.9%
-75.3 1
1.9%
-75.29 1
1.9%
-74.99 1
1.9%
-72.19 1
1.9%
-71.97 1
1.9%
-52.86 1
1.9%
ValueCountFrequency (%)
-0.01 1
1.9%
-36.94 1
1.9%
-52.86 1
1.9%
-71.97 1
1.9%
-72.19 1
1.9%
-74.99 1
1.9%
-75.29 1
1.9%
-75.3 1
1.9%
-78.58 1
1.9%
-82.72 1
1.9%

Non-energy
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Total Secundaries
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23961.181
Minimum3613.39
Maximum46508.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:36:48.394895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3613.39
5-th percentile4906.9575
Q112936.685
median22951.87
Q335161.013
95-th percentile43475.643
Maximum46508.5
Range42895.11
Interquartile range (IQR)22224.328

Descriptive statistics

Standard deviation13081.425
Coefficient of variation (CV)0.5459424
Kurtosis-1.2132818
Mean23961.181
Median Absolute Deviation (MAD)11309.015
Skewness0.13521297
Sum1245981.4
Variance1.7112367 × 108
MonotonicityNot monotonic
2023-07-30T07:36:48.637814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3613.39 1
 
1.9%
4112.59 1
 
1.9%
25829.2 1
 
1.9%
26707.88 1
 
1.9%
27844.17 1
 
1.9%
25902.98 1
 
1.9%
26724.24 1
 
1.9%
28240.64 1
 
1.9%
29985.81 1
 
1.9%
31156.29 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
3613.39 1
1.9%
4112.59 1
1.9%
4564.17 1
1.9%
5187.42 1
1.9%
5768.89 1
1.9%
6350.11 1
1.9%
7266.76 1
1.9%
8173.78 1
1.9%
9106.34 1
1.9%
10223.54 1
1.9%
ValueCountFrequency (%)
46508.5 1
1.9%
44947.39 1
1.9%
44163.05 1
1.9%
42913.22 1
1.9%
42721.55 1
1.9%
42133.96 1
1.9%
41721.75 1
1.9%
41633.34 1
1.9%
41208.58 1
1.9%
40825.1 1
1.9%

Total
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4857.2712
Minimum-17268.03
Maximum-741.46
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)100.0%
Memory size548.0 B
2023-07-30T07:36:48.884610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-17268.03
5-th percentile-14528.755
Q1-7065.0925
median-2289.18
Q3-1171.415
95-th percentile-852.955
Maximum-741.46
Range16526.57
Interquartile range (IQR)5893.6775

Descriptive statistics

Standard deviation4611.7298
Coefficient of variation (CV)-0.94944871
Kurtosis0.3244992
Mean-4857.2712
Median Absolute Deviation (MAD)1436.91
Skewness-1.1333041
Sum-252578.1
Variance21268052
MonotonicityNot monotonic
2023-07-30T07:36:49.185937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-911.74 1
 
1.9%
-1189.52 1
 
1.9%
-2411.4 1
 
1.9%
-4584.74 1
 
1.9%
-5993.15 1
 
1.9%
-7813 1
 
1.9%
-5897.93 1
 
1.9%
-5633.93 1
 
1.9%
-6224.1 1
 
1.9%
-6139.58 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
-17268.03 1
1.9%
-16241.63 1
1.9%
-14785.66 1
1.9%
-14318.56 1
1.9%
-12106.84 1
1.9%
-11514.7 1
1.9%
-11499.83 1
1.9%
-10378.08 1
1.9%
-10093.73 1
1.9%
-9548.89 1
1.9%
ValueCountFrequency (%)
-741.46 1
1.9%
-825.95 1
1.9%
-845.42 1
1.9%
-859.12 1
1.9%
-870.29 1
1.9%
-892.7 1
1.9%
-903.3 1
1.9%
-911.74 1
1.9%
-933.81 1
1.9%
-1009.15 1
1.9%

Interactions

2023-07-30T07:36:36.857280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:35:54.523424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:35:58.891541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:01.815527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:06.084940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:09.023757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:11.752430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:14.598415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:18.139599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:21.492423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:24.280600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:27.188928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:30.441870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:34.053859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:37.257196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:35:54.997813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:35:59.209233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:02.231718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:06.498048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:09.338803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:11.984940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:14.847153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:18.825886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:21.887356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:24.685457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:27.611200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:30.773191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:34.467289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:37.456813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:35:55.283675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:35:59.432892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:02.424181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:06.702182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:09.533197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:12.187837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:15.064131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:19.184999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:22.090443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:24.875163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:27.810451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:31.085993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:34.656260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:37.654701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:35:55.623714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:35:59.626548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:02.737270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:06.893366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:09.704137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:12.390666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:15.254989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:19.457907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:22.278733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:25.078461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:28.000273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:31.408126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:34.850115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:37.872801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:35:55.953401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:35:59.836061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:03.053143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:07.093711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:09.880218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:12.577412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:15.448957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:19.637334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:22.477987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:25.273375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:28.204760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:31.724641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:35.031126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:38.050140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:35:56.228190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:00.013728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:03.363127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:07.253272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:10.046772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:12.767876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:15.661192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:19.804620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:22.656329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:25.456620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:28.372818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:32.052675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:35.206902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:38.257957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:35:56.461245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:00.211054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:03.670281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:07.455386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:10.240938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:12.984325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:15.849482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:19.989744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:22.863305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:25.649998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:28.581765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:32.379647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:35.405647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:38.460073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:35:56.708088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:00.419564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:03.965911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:07.660811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:10.461129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:13.222260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:16.076611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:20.181090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:23.060898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:25.851228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:28.768713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:32.678553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:35.601005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:38.643591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:35:57.048181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:00.623335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:04.256741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:07.845939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:10.638022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:13.404283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:16.344385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:20.380842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:23.236244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:26.050440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:28.958755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:32.888805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:35.792852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:38.816706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:35:57.364361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:00.814571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:04.557370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:08.038856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:10.814702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:13.605046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:16.653137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:20.544248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:23.406907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:26.232027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:29.132985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:33.073048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:35.949974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:39.031566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:35:57.692141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:01.008856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:04.890784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:08.253463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:10.991644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:13.783636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:16.968353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:20.729795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:23.582843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:26.417686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:29.325582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:33.284495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:36.127586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:39.219157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:35:58.024840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:01.190137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:05.214081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:08.438825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:11.172577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:13.977782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:17.235698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:20.904254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:23.755743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:26.614633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:29.553008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:33.475505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:36.301016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:39.412333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:35:58.239697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:01.380544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:05.517808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:08.640151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:11.392812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:14.192580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:17.492881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:21.112452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:23.941527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:26.802089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:29.835080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:33.690117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:36.495715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:39.581409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:35:58.557141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:01.591376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:05.777441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:08.811469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:11.571980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:14.406126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:17.811089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:21.273467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:24.098577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:26.976429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:30.123858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:33.880039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:36.649933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-30T07:36:49.420982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
YearNatural gasCoalHydroenergyNuclearFirewoodOther Primary_x000d_Total PrimariesElectricityDiesel oilFuel oilOther secondaryTotal SecundariesTotal
Year1.000-0.937-0.916-0.969-0.903-0.746-1.000-0.9970.999-0.865-0.288-0.6220.999-0.949
Natural gas-0.9371.0000.7490.7970.8960.6960.8330.954-0.9440.6440.4670.636-0.9440.958
Coal-0.9160.7491.0000.8600.7360.7160.5770.925-0.9190.8240.3770.503-0.9190.936
Hydroenergy-0.9690.7970.8601.0000.8570.3190.6170.965-0.9690.8890.253-0.566-0.9690.909
Nuclear-0.9030.8960.7360.8571.0000.5120.7270.899-0.8960.6380.3400.378-0.8960.895
Firewood-0.7460.6960.7160.3190.5121.0000.5710.798-0.8020.4770.2820.769-0.8020.746
Other Primary_x000d_\n-1.0000.8330.5770.6170.7270.5711.0000.961-0.985-0.1900.0740.622-0.9850.796
Total Primaries-0.9970.9540.9250.9650.8990.7980.9611.000-0.9990.8830.3060.720-0.9990.955
Electricity0.999-0.944-0.919-0.969-0.896-0.802-0.985-0.9991.000-0.875-0.292-0.7341.000-0.951
Diesel oil-0.8650.6440.8240.8890.6380.477-0.1900.883-0.8751.0000.374-0.287-0.8750.869
Fuel oil-0.2880.4670.3770.2530.3400.2820.0740.306-0.2920.3741.0000.147-0.2920.470
Other secondary-0.6220.6360.503-0.5660.3780.7690.6220.720-0.734-0.2870.1471.000-0.7340.580
Total Secundaries0.999-0.944-0.919-0.969-0.896-0.802-0.985-0.9991.000-0.875-0.292-0.7341.000-0.951
Total-0.9490.9580.9360.9090.8950.7460.7960.955-0.9510.8690.4700.580-0.9511.000

Missing values

2023-07-30T07:36:39.950096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-30T07:36:40.533937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-30T07:36:40.890361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

395YearOilNatural gasCoalHydroenergyNuclearFirewoodSugarcane and productsOther Primary_x000d_Total PrimariesElectricityLPGGasoline/alcoholKerosene/jet fuelDiesel oilFuel oilCokeCharcoalGasesOther secondaryNon-energyTotal SecundariesTotal
11970NaNNaN-454.51-3302.28NaNNaNNaNNaN-3756.793613.39NaNNaNNaN-115.74-652.61NaNNaNNaNNaNNaN3613.39-911.74
21971NaNNaN-473.18-3599.12NaNNaNNaNNaN-4072.304112.59NaNNaNNaN-111.45-1118.36NaNNaNNaNNaNNaN4112.59-1189.52
31972NaNNaN-504.88-4232.69NaNNaNNaNNaN-4737.564564.17NaNNaNNaN-119.17-600.14NaNNaNNaNNaNNaN4564.17-892.70
41973NaNNaN-444.92-4839.00NaNNaNNaNNaN-5283.925187.42NaNNaNNaN-118.31-815.53NaNNaNNaNNaNNaN5187.42-1030.34
51974NaNNaN-426.03-5500.16NaNNaNNaNNaN-5926.205768.89NaNNaNNaN-119.17-582.65NaNNaNNaNNaNNaN5768.89-859.12
61975NaNNaN-412.71-6050.86NaNNaNNaNNaN-6463.576350.11NaNNaNNaN-124.31-588.18NaNNaNNaNNaNNaN6350.11-825.95
71976NaNNaN-401.51-6935.88NaNNaNNaNNaN-7337.387266.76NaNNaNNaN-148.32-522.52NaNNaNNaNNaNNaN7266.76-741.46
81977NaNNaN-518.12-7812.81NaNNaNNaNNaN-8330.938173.78NaNNaNNaN-137.17-608.98NaNNaNNaNNaNNaN8173.78-903.30
91978NaNNaN-883.91-8596.22NaNNaNNaNNaN-9480.139106.34NaNNaNNaN-167.18-753.71NaNNaNNaNNaNNaN9106.34-1294.68
101979NaNNaN-734.70-9795.95NaNNaNNaNNaN-10530.6510223.54NaNNaNNaN-264.91-545.08NaNNaNNaNNaNNaN10223.54-1117.10
395YearOilNatural gasCoalHydroenergyNuclearFirewoodSugarcane and productsOther Primary_x000d_Total PrimariesElectricityLPGGasoline/alcoholKerosene/jet fuelDiesel oilFuel oilCokeCharcoalGasesOther secondaryNon-energyTotal SecundariesTotal
432012NaN-6682.24-2176.23-33976.84-4179.70-44.97NaN-520.89-47580.8840825.10NaNNaNNaN-2257.77-1027.32NaNNaNNaN-52.86NaN40825.10-10093.73
442013NaN-11543.61-3559.56-31744.82-4026.28-64.16NaN-625.21-51563.6541633.34NaNNaNNaN-2345.34-1967.62NaNNaNNaN-75.29NaN41633.34-14318.56
452014NaN-14183.44-4129.61-30133.97-4007.72-67.72NaN-1166.92-53689.3842721.55NaNNaNNaN-2907.78-3313.85NaNNaNNaN-78.58NaN42721.55-17268.03
462015NaN-13670.34-4253.56-29046.64-3839.81-65.73NaN-2003.05-52879.1341721.75NaNNaNNaN-2027.54-2981.73NaNNaNNaN-74.99NaN41721.75-16241.63
472016NaN-8717.87-3927.43-30840.26-4134.34-43.14NaN-3028.94-50691.9941208.58NaNNaNNaN-831.65-1127.44NaNNaNNaN-72.19NaN41208.58-11514.70
482017NaN-10325.21-3544.06-30223.82-4101.73-63.25NaN-3895.16-52153.2342133.96NaNNaNNaN-629.15-1375.41NaNNaNNaN-83.01NaN42133.96-12106.84
492018NaN-8069.18-3046.29-31710.29-4084.71-68.80NaN-4620.22-51599.4942913.22NaNNaNNaN-891.36-717.72NaNNaNNaN-82.72NaN42913.22-10378.08
502019NaN-9362.71-3329.31-32465.96-4203.27-71.39NaN-5499.64-54932.2744947.39NaNNaNNaN-1111.70-265.93NaNNaNNaN-137.32NaN44947.39-11499.83
512020NaN-7793.97-2601.86-32482.00-3665.67-65.58NaN-5676.99-52286.0644163.05NaNNaNNaN-1069.18-284.73NaNNaNNaN-71.97NaN44163.05-9548.89
522021NaN-13219.00-4189.67-29752.22-3835.75-56.11NaN-7138.42-58191.1646508.50NaNNaNNaN-1371.21-1656.49NaNNaNNaN-75.30NaN46508.50-14785.66